Mathew Vis-Dunbar
October, 2022
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##
## The decimal point is 2 digit(s) to the right of the |
##
## 1 | 4
## 2 | 0112233345555666677788888999
## 3 | 00001111223333344455555666688888999
## 4 | 111222333445566779
## 5 | 001233344567
## 6 | 000112233578
## 7 | 012234468
## 8 | 04579
## 9 | 0018
## 10 | 045
## 11 | 07
## 12 | 147
## 13 | 1
## 14 | 56
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## 17 | 7
## 18 | 9
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##
## The decimal point is 2 digit(s) to the right of the |
##
## 1 | 4
## 2 | 0112233345555666677788888999
## 3 | 00001111223333344455555666688888999
## 4 | 111222333445566779
## 5 | 001233344567
## 6 | 000112233578
## 7 | 012234468
## 8 | 04579
## 9 | 0018
## 10 | 045
## 11 | 07
## 12 | 147
## 13 | 1
## 14 | 56
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## 17 | 7
## 18 | 9
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##
## The decimal point is 2 digit(s) to the right of the |
##
## 1 | 4
## 2 | 0112233345555666677788888999
## 3 | 00001111223333344455555666688888999
## 4 | 111222333445566779
## 5 | 001233344567
## 6 | 000112233578
## 7 | 012234468
## 8 | 04579
## 9 | 0018
## 10 | 045
## 11 | 07
## 12 | 147
## 13 | 1
## 14 | 56
## 15 |
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## 17 | 7
## 18 | 9
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## 23 | 25
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##
## The decimal point is 2 digit(s) to the right of the |
##
## 1 | 4
## 2 | 0112233345555666677788888999
## 3 | 00001111223333344455555666688888999
## 4 | 111222333445566779
## 5 | 001233344567
## 6 | 000112233578
## 7 | 012234468
## 8 | 04579
## 9 | 0018
## 10 | 045
## 11 | 07
## 12 | 147
## 13 | 1
## 14 | 56
## 15 |
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## 17 | 7
## 18 | 9
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##
## The decimal point is 2 digit(s) to the right of the |
##
## 1 | 4
## 2 | 0112233345555666677788888999
## 3 | 00001111223333344455555666688888999
## 4 | 111222333445566779
## 5 | 001233344567
## 6 | 000112233578
## 7 | 012234468
## 8 | 04579
## 9 | 0018
## 10 | 045
## 11 | 07
## 12 | 147
## 13 | 1
## 14 | 56
## 15 |
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## 17 | 7
## 18 | 9
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## 23 | 25
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135 202 210 210 215 217 230 230 233 237 246 250 250 250 255 259 260 260 265 268 270 276 280 280 280 281 286 290 291 300 300 300 301 306 310 310 314 315 320 325 327 329 330 332 336 338 340 350 350 350 350 352 360 360 360 360 375 377 380 380 383 390 390 392 407 410 411 420 420 424 425 430 431 435 444 445 450 460 460 465 470 490 500 500 505 524 525 525 529 538 540 545 560 570 600 600 600 605 610 618 620 625 630 652 671 680 696 710 720 720 730 735 735 760 780 800 840 850 870 890 900 900 906 981 1000 1038 1054 1100 1171 1205 1243 1270 1306 1450 1459 1770 1885 2315 2348 2533 3710
##
## The decimal point is 2 digit(s) to the right of the |
##
## 1 | 4
## 2 | 0112233345555666677788888999
## 3 | 00001111223333344455555666688888999
## 4 | 111222333445566779
## 5 | 001233344567
## 6 | 000112233578
## 7 | 012234468
## 8 | 04579
## 9 | 0018
## 10 | 045
## 11 | 07
## 12 | 147
## 13 | 1
## 14 | 56
## 15 |
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## 17 | 7
## 18 | 9
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135 202 210 210 215 217 230 230 233 237 246 250 250 250 255 259 260 260 265 268 270 276 280 280 280 281 286 290 291 300 300 300 301 306 310 310 314 315 320 325 327 329 330 332 336 338 340 350 350 350 350 352 360 360 360 360 375 377 380 380 383 390 390 392 407 410 411 420 420 424 425 430 431 435 444 445 450 460 460 465 470 490 500 500 505 524 525 525 529 538 540 545 560 570 600 600 600 605 610 618 620 625 630 652 671 680 696 710 720 720 730 735 735 760 780 800 840 850 870 890 900 900 906 981 1000 1038 1054 1100 1171 1205 1243 1270 1306 1450 1459 1770 1885 2315 2348 2533 3710
##
## The decimal point is 2 digit(s) to the right of the |
##
## 1 | 4
## 2 | 0112233345555666677788888999
## 3 | 00001111223333344455555666688888999
## 4 | 111222333445566779
## 5 | 001233344567
## 6 | 000112233578
## 7 | 012234468
## 8 | 04579
## 9 | 0018
## 10 | 045
## 11 | 07
## 12 | 147
## 13 | 1
## 14 | 56
## 15 |
## 16 |
## 17 | 7
## 18 | 9
## 19 |
## 20 |
## 21 |
## 22 |
## 23 | 25
## 24 |
## 25 | 3
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##
## The decimal point is 2 digit(s) to the right of the |
##
## 1 | 4
## 2 | 0112233345555666677788888999
## 3 | 00001111223333344455555666688888999
## 4 | 111222333445566779
## 5 | 001233344567
## 6 | 000112233578
## 7 | 012234468
## 8 | 04579
## 9 | 0018
## 10 | 045
## 11 | 07
## 12 | 147
## 13 | 1
## 14 | 56
## 15 |
## 16 |
## 17 | 7
## 18 | 9
## 19 |
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## 23 | 25
## 24 |
## 25 | 3
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## 37 | 1
Data visualization turns data into visual information. We could turn data into auditory or tactile information as well.
Data visualization turns data into visual information. We could turn data into auditory or tactile information as well.
This involves abstraction as shapes, colours etc are used represent the data.
Data visualization turns data into visual information. We could turn data into auditory or tactile information as well.
This involves abstraction as shapes, colours etc which are used represent the data.
Visual and data literacies are needed to interpret both the data and the abstraction.
Data visualization is the graphical display of abstract information for two purposes: sense-making (also called data analysis) and communication.
Stephen Few. Data Visualization for Human Perception.
Even though an object as a whole might take some conscious effort to identify, the basic visual attributes that combine to make up that object are perceived without any conscious effort.
Stephen Few (2004). Tapping the Power of Visual Perception.
Nominal data
No order
Ordinal data
Intrinsic order
Nominal data
No order
Ordinal data
Intrinsic order
Visualization tool
Frequency plots with bar charts
| species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex | year |
|---|---|---|---|---|---|---|---|
| Chinstrap | Dream | 43.5 | 18.1 | 202 | 3400 | female | 2009 |
| Gentoo | Biscoe | 43.6 | 13.9 | 217 | 4900 | female | 2008 |
| Gentoo | Biscoe | 48.1 | 15.1 | 209 | 5500 | male | 2009 |
| Gentoo | Biscoe | 46.8 | 14.3 | 215 | 4850 | female | 2009 |
| Gentoo | Biscoe | 48.5 | 14.1 | 220 | 5300 | male | 2008 |
| Chinstrap | Dream | 42.4 | 17.3 | 181 | 3600 | female | 2007 |
| Gentoo | Biscoe | 46.1 | 15.1 | 215 | 5100 | male | 2007 |
| Adelie | Torgersen | 35.5 | 17.5 | 190 | 3700 | female | 2008 |
| Adelie | Torgersen | 38.5 | 17.9 | 190 | 3325 | female | 2009 |
| Chinstrap | Dream | 52.8 | 20.0 | 205 | 4550 | male | 2008 |
| Chinstrap | Dream | 49.7 | 18.6 | 195 | 3600 | male | 2008 |
| Adelie | Biscoe | 37.6 | 17.0 | 185 | 3600 | female | 2008 |
| Adelie | Dream | 41.1 | 17.5 | 190 | 3900 | male | 2009 |
| Gentoo | Biscoe | 45.2 | 13.8 | 215 | 4750 | female | 2008 |
| Chinstrap | Dream | 47.0 | 17.3 | 185 | 3700 | female | 2007 |
| Adelie | Biscoe | 42.0 | 19.5 | 200 | 4050 | male | 2008 |
| Adelie | Biscoe | 45.6 | 20.3 | 191 | 4600 | male | 2009 |
| Gentoo | Biscoe | 46.2 | 14.5 | 209 | 4800 | female | 2007 |
| Adelie | Dream | 42.2 | 18.5 | 180 | 3550 | female | 2007 |
| Adelie | Biscoe | 43.2 | 19.0 | 197 | 4775 | male | 2009 |
| species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex | year |
|---|---|---|---|---|---|---|---|
| Chinstrap | Dream | 43.5 | 18.1 | 202 | 3400 | female | 2009 |
| Gentoo | Biscoe | 43.6 | 13.9 | 217 | 4900 | female | 2008 |
| Gentoo | Biscoe | 48.1 | 15.1 | 209 | 5500 | male | 2009 |
| Gentoo | Biscoe | 46.8 | 14.3 | 215 | 4850 | female | 2009 |
| Gentoo | Biscoe | 48.5 | 14.1 | 220 | 5300 | male | 2008 |
| Chinstrap | Dream | 42.4 | 17.3 | 181 | 3600 | female | 2007 |
| Gentoo | Biscoe | 46.1 | 15.1 | 215 | 5100 | male | 2007 |
| Adelie | Torgersen | 35.5 | 17.5 | 190 | 3700 | female | 2008 |
| Adelie | Torgersen | 38.5 | 17.9 | 190 | 3325 | female | 2009 |
| Chinstrap | Dream | 52.8 | 20.0 | 205 | 4550 | male | 2008 |
| Chinstrap | Dream | 49.7 | 18.6 | 195 | 3600 | male | 2008 |
| Adelie | Biscoe | 37.6 | 17.0 | 185 | 3600 | female | 2008 |
| Adelie | Dream | 41.1 | 17.5 | 190 | 3900 | male | 2009 |
| Gentoo | Biscoe | 45.2 | 13.8 | 215 | 4750 | female | 2008 |
| Chinstrap | Dream | 47.0 | 17.3 | 185 | 3700 | female | 2007 |
| Adelie | Biscoe | 42.0 | 19.5 | 200 | 4050 | male | 2008 |
| Adelie | Biscoe | 45.6 | 20.3 | 191 | 4600 | male | 2009 |
| Gentoo | Biscoe | 46.2 | 14.5 | 209 | 4800 | female | 2007 |
| Adelie | Dream | 42.2 | 18.5 | 180 | 3550 | female | 2007 |
| Adelie | Biscoe | 43.2 | 19.0 | 197 | 4775 | male | 2009 |
| carat | cut | color | clarity | depth | table | price | x | y | z |
|---|---|---|---|---|---|---|---|---|---|
| 0.57 | Ideal | G | VS2 | 61.6 | 57.0 | 1728 | 5.36 | 5.32 | 3.29 |
| 1.01 | Ideal | D | SI2 | 61.3 | 54.0 | 4916 | 6.47 | 6.52 | 3.99 |
| 0.45 | Fair | F | VS2 | 67.0 | 56.0 | 923 | 4.77 | 4.70 | 3.17 |
| 1.04 | Premium | H | VVS2 | 59.1 | 60.0 | 6278 | 6.66 | 6.60 | 3.92 |
| 0.90 | Very Good | G | VVS2 | 59.8 | 60.0 | 5102 | 6.23 | 6.28 | 3.74 |
| 1.20 | Very Good | I | VS2 | 62.3 | 56.0 | 5955 | 6.76 | 6.81 | 4.23 |
| 1.25 | Premium | F | I1 | 58.0 | 59.0 | 3724 | 7.12 | 7.05 | 4.11 |
| 1.52 | Premium | G | VVS2 | 62.1 | 58.0 | 14105 | 7.40 | 7.31 | 4.57 |
| 1.03 | Very Good | D | SI2 | 61.3 | 61.0 | 4679 | 6.46 | 6.49 | 3.97 |
| 0.44 | Premium | E | SI1 | 62.3 | 58.0 | 778 | 4.85 | 4.88 | 3.03 |
| 1.13 | Ideal | E | VVS2 | 60.1 | 59.0 | 11387 | 6.77 | 6.81 | 4.08 |
| 1.11 | Good | H | SI2 | 63.5 | 60.3 | 4456 | 6.49 | 6.58 | 4.15 |
| 1.00 | Fair | H | VS2 | 64.8 | 62.0 | 4861 | 6.22 | 6.13 | 4.00 |
| 0.44 | Very Good | H | IF | 61.1 | 57.0 | 1194 | 4.91 | 4.94 | 3.01 |
| 1.12 | Ideal | G | VS1 | 61.0 | 56.0 | 7632 | 6.76 | 6.72 | 4.11 |
| 0.91 | Ideal | H | VS2 | 62.4 | 56.0 | 4678 | 6.20 | 6.14 | 3.85 |
| 1.50 | Premium | D | SI2 | 62.6 | 59.0 | 8108 | 7.20 | 7.23 | 4.52 |
| 1.03 | Premium | E | SI2 | 61.8 | 59.0 | 4845 | 6.47 | 6.42 | 3.98 |
| 0.71 | Fair | F | SI1 | 59.8 | 70.0 | 2458 | 5.89 | 5.72 | 3.48 |
| 1.32 | Ideal | E | VS2 | 62.0 | 56.0 | 10919 | 7.02 | 7.07 | 4.37 |
| carat | cut | color | clarity | depth | table | price | x | y | z |
|---|---|---|---|---|---|---|---|---|---|
| 0.57 | Ideal | G | VS2 | 61.6 | 57.0 | 1728 | 5.36 | 5.32 | 3.29 |
| 1.01 | Ideal | D | SI2 | 61.3 | 54.0 | 4916 | 6.47 | 6.52 | 3.99 |
| 0.45 | Fair | F | VS2 | 67.0 | 56.0 | 923 | 4.77 | 4.70 | 3.17 |
| 1.04 | Premium | H | VVS2 | 59.1 | 60.0 | 6278 | 6.66 | 6.60 | 3.92 |
| 0.90 | Very Good | G | VVS2 | 59.8 | 60.0 | 5102 | 6.23 | 6.28 | 3.74 |
| 1.20 | Very Good | I | VS2 | 62.3 | 56.0 | 5955 | 6.76 | 6.81 | 4.23 |
| 1.25 | Premium | F | I1 | 58.0 | 59.0 | 3724 | 7.12 | 7.05 | 4.11 |
| 1.52 | Premium | G | VVS2 | 62.1 | 58.0 | 14105 | 7.40 | 7.31 | 4.57 |
| 1.03 | Very Good | D | SI2 | 61.3 | 61.0 | 4679 | 6.46 | 6.49 | 3.97 |
| 0.44 | Premium | E | SI1 | 62.3 | 58.0 | 778 | 4.85 | 4.88 | 3.03 |
| 1.13 | Ideal | E | VVS2 | 60.1 | 59.0 | 11387 | 6.77 | 6.81 | 4.08 |
| 1.11 | Good | H | SI2 | 63.5 | 60.3 | 4456 | 6.49 | 6.58 | 4.15 |
| 1.00 | Fair | H | VS2 | 64.8 | 62.0 | 4861 | 6.22 | 6.13 | 4.00 |
| 0.44 | Very Good | H | IF | 61.1 | 57.0 | 1194 | 4.91 | 4.94 | 3.01 |
| 1.12 | Ideal | G | VS1 | 61.0 | 56.0 | 7632 | 6.76 | 6.72 | 4.11 |
| 0.91 | Ideal | H | VS2 | 62.4 | 56.0 | 4678 | 6.20 | 6.14 | 3.85 |
| 1.50 | Premium | D | SI2 | 62.6 | 59.0 | 8108 | 7.20 | 7.23 | 4.52 |
| 1.03 | Premium | E | SI2 | 61.8 | 59.0 | 4845 | 6.47 | 6.42 | 3.98 |
| 0.71 | Fair | F | SI1 | 59.8 | 70.0 | 2458 | 5.89 | 5.72 | 3.48 |
| 1.32 | Ideal | E | VS2 | 62.0 | 56.0 | 10919 | 7.02 | 7.07 | 4.37 |
Summary count of Penguins by species. n = 344
| carat | cut | color | clarity | depth | table | price | x | y | z |
|---|---|---|---|---|---|---|---|---|---|
| 0.57 | Ideal | G | VS2 | 61.6 | 57.0 | 1728 | 5.36 | 5.32 | 3.29 |
| 1.01 | Ideal | D | SI2 | 61.3 | 54.0 | 4916 | 6.47 | 6.52 | 3.99 |
| 0.45 | Fair | F | VS2 | 67.0 | 56.0 | 923 | 4.77 | 4.70 | 3.17 |
| 1.04 | Premium | H | VVS2 | 59.1 | 60.0 | 6278 | 6.66 | 6.60 | 3.92 |
| 0.90 | Very Good | G | VVS2 | 59.8 | 60.0 | 5102 | 6.23 | 6.28 | 3.74 |
| 1.20 | Very Good | I | VS2 | 62.3 | 56.0 | 5955 | 6.76 | 6.81 | 4.23 |
| 1.25 | Premium | F | I1 | 58.0 | 59.0 | 3724 | 7.12 | 7.05 | 4.11 |
| 1.52 | Premium | G | VVS2 | 62.1 | 58.0 | 14105 | 7.40 | 7.31 | 4.57 |
| 1.03 | Very Good | D | SI2 | 61.3 | 61.0 | 4679 | 6.46 | 6.49 | 3.97 |
| 0.44 | Premium | E | SI1 | 62.3 | 58.0 | 778 | 4.85 | 4.88 | 3.03 |
| 1.13 | Ideal | E | VVS2 | 60.1 | 59.0 | 11387 | 6.77 | 6.81 | 4.08 |
| 1.11 | Good | H | SI2 | 63.5 | 60.3 | 4456 | 6.49 | 6.58 | 4.15 |
| 1.00 | Fair | H | VS2 | 64.8 | 62.0 | 4861 | 6.22 | 6.13 | 4.00 |
| 0.44 | Very Good | H | IF | 61.1 | 57.0 | 1194 | 4.91 | 4.94 | 3.01 |
| 1.12 | Ideal | G | VS1 | 61.0 | 56.0 | 7632 | 6.76 | 6.72 | 4.11 |
| 0.91 | Ideal | H | VS2 | 62.4 | 56.0 | 4678 | 6.20 | 6.14 | 3.85 |
| 1.50 | Premium | D | SI2 | 62.6 | 59.0 | 8108 | 7.20 | 7.23 | 4.52 |
| 1.03 | Premium | E | SI2 | 61.8 | 59.0 | 4845 | 6.47 | 6.42 | 3.98 |
| 0.71 | Fair | F | SI1 | 59.8 | 70.0 | 2458 | 5.89 | 5.72 | 3.48 |
| 1.32 | Ideal | E | VS2 | 62.0 | 56.0 | 10919 | 7.02 | 7.07 | 4.37 |
Discrete = Counted
Continuous = Measured
Interval = Greater or less than
Ratio = Percentage more or less
Visualization tool
Frequency plots with histograms
| species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex | year |
|---|---|---|---|---|---|---|---|
| Chinstrap | Dream | 43.5 | 18.1 | 202 | 3400 | female | 2009 |
| Gentoo | Biscoe | 43.6 | 13.9 | 217 | 4900 | female | 2008 |
| Gentoo | Biscoe | 48.1 | 15.1 | 209 | 5500 | male | 2009 |
| Gentoo | Biscoe | 46.8 | 14.3 | 215 | 4850 | female | 2009 |
| Gentoo | Biscoe | 48.5 | 14.1 | 220 | 5300 | male | 2008 |
| Chinstrap | Dream | 42.4 | 17.3 | 181 | 3600 | female | 2007 |
| Gentoo | Biscoe | 46.1 | 15.1 | 215 | 5100 | male | 2007 |
| Adelie | Torgersen | 35.5 | 17.5 | 190 | 3700 | female | 2008 |
| Adelie | Torgersen | 38.5 | 17.9 | 190 | 3325 | female | 2009 |
| Chinstrap | Dream | 52.8 | 20.0 | 205 | 4550 | male | 2008 |
| Chinstrap | Dream | 49.7 | 18.6 | 195 | 3600 | male | 2008 |
| Adelie | Biscoe | 37.6 | 17.0 | 185 | 3600 | female | 2008 |
| Adelie | Dream | 41.1 | 17.5 | 190 | 3900 | male | 2009 |
| Gentoo | Biscoe | 45.2 | 13.8 | 215 | 4750 | female | 2008 |
| Chinstrap | Dream | 47.0 | 17.3 | 185 | 3700 | female | 2007 |
| Adelie | Biscoe | 42.0 | 19.5 | 200 | 4050 | male | 2008 |
| Adelie | Biscoe | 45.6 | 20.3 | 191 | 4600 | male | 2009 |
| Gentoo | Biscoe | 46.2 | 14.5 | 209 | 4800 | female | 2007 |
| Adelie | Dream | 42.2 | 18.5 | 180 | 3550 | female | 2007 |
| Adelie | Biscoe | 43.2 | 19.0 | 197 | 4775 | male | 2009 |
Dot Plots, Line Graphs & Scatter Plots
| Country | Country_Code | Year | Life_Expectency |
|---|---|---|---|
| Honduras | HND | 2016 | 73.57500 |
| El Salvador | SLV | 2016 | 73.51200 |
| Algeria | DZA | 2016 | 76.07800 |
| Guinea | GIN | 2016 | 60.01500 |
| Turkey | TUR | 2016 | 75.75500 |
| Mongolia | MNG | 2016 | 69.28700 |
| Zambia | ZMB | 2016 | 61.87400 |
| Ethiopia | ETH | 2016 | 65.47500 |
| South Sudan | SSD | 2016 | 56.81100 |
| Gabon | GAB | 2016 | 66.10500 |
| Lithuania | LTU | 2016 | 74.32195 |
| Norway | NOR | 2016 | 82.50976 |
| Sierra Leone | SLE | 2016 | 51.83500 |
| North Macedonia | MKD | 2016 | 75.70300 |
| Israel | ISR | 2016 | 82.40732 |
| China | CHN | 2016 | 76.25200 |
| Slovenia | SVN | 2016 | 80.77561 |
| Mali | MLI | 2016 | 57.96600 |
| Dominican Republic | DOM | 2016 | 73.86100 |
| Liechtenstein | LIE | 2016 | 82.65610 |
| Country | Country_Code | Year | Life_Expectency |
|---|---|---|---|
| Honduras | HND | 2016 | 73.57500 |
| El Salvador | SLV | 2016 | 73.51200 |
| Algeria | DZA | 2016 | 76.07800 |
| Guinea | GIN | 2016 | 60.01500 |
| Turkey | TUR | 2016 | 75.75500 |
| Mongolia | MNG | 2016 | 69.28700 |
| Zambia | ZMB | 2016 | 61.87400 |
| Ethiopia | ETH | 2016 | 65.47500 |
| South Sudan | SSD | 2016 | 56.81100 |
| Gabon | GAB | 2016 | 66.10500 |
| Lithuania | LTU | 2016 | 74.32195 |
| Norway | NOR | 2016 | 82.50976 |
| Sierra Leone | SLE | 2016 | 51.83500 |
| North Macedonia | MKD | 2016 | 75.70300 |
| Israel | ISR | 2016 | 82.40732 |
| China | CHN | 2016 | 76.25200 |
| Slovenia | SVN | 2016 | 80.77561 |
| Mali | MLI | 2016 | 57.96600 |
| Dominican Republic | DOM | 2016 | 73.86100 |
| Liechtenstein | LIE | 2016 | 82.65610 |
| Country | Code | Year | Population | Continent | Life.Expectency | GDP |
|---|---|---|---|---|---|---|
| Lesotho | LSO | 2015 | 2059000 | Africa | 51.038 | 2954 |
| Armenia | ARM | 2015 | 2926000 | Asia | 74.467 | 9552 |
| Uruguay | URY | 2015 | 3412000 | South America | 77.369 | 19668 |
| Slovakia | SVK | 2015 | 5436000 | Europe | 76.827 | 25896 |
| Bosnia and Herzegovina | BIH | 2015 | 3429000 | Europe | 76.865 | 10305 |
| Mali | MLI | 2015 | 17439000 | Africa | 57.509 | 1563 |
| Romania | ROU | 2015 | 19925000 | Europe | 75.476 | 20549 |
| Denmark | DNK | 2015 | 5689000 | Europe | 80.475 | 44939 |
| Jamaica | JAM | 2015 | 2891000 | North America | 74.098 | 7115 |
| Senegal | SEN | 2015 | 14578000 | Africa | 66.747 | 2446 |
| Eswatini | SWZ | 2015 | 1104000 | Africa | 55.359 | 7726 |
| Pakistan | PAK | 2015 | 199427008 | Asia | 66.577 | 5056 |
| Gambia | GMB | 2015 | 2086000 | Africa | 60.910 | 1948 |
| Haiti | HTI | 2015 | 10696000 | North America | 62.485 | 1649 |
| Vietnam | VNM | 2015 | 92677000 | Asia | 75.110 | 5733 |
| Congo | COG | 2015 | 4856000 | Africa | 63.097 | 4526 |
| Gabon | GAB | 2015 | 1948000 | Africa | 64.913 | 14315 |
| Nepal | NPL | 2015 | 27015000 | Asia | 69.515 | 2607 |
| North Macedonia | MKD | 2015 | 2079000 | Europe | 75.406 | 13586 |
| Cyprus | CYP | 2015 | 1161000 | Europe | 80.350 | 25903 |
| Country | Code | Year | Population | Continent | Life.Expectency | GDP |
|---|---|---|---|---|---|---|
| Lesotho | LSO | 2015 | 2059000 | Africa | 51.038 | 2954 |
| Armenia | ARM | 2015 | 2926000 | Asia | 74.467 | 9552 |
| Uruguay | URY | 2015 | 3412000 | South America | 77.369 | 19668 |
| Slovakia | SVK | 2015 | 5436000 | Europe | 76.827 | 25896 |
| Bosnia and Herzegovina | BIH | 2015 | 3429000 | Europe | 76.865 | 10305 |
| Mali | MLI | 2015 | 17439000 | Africa | 57.509 | 1563 |
| Romania | ROU | 2015 | 19925000 | Europe | 75.476 | 20549 |
| Denmark | DNK | 2015 | 5689000 | Europe | 80.475 | 44939 |
| Jamaica | JAM | 2015 | 2891000 | North America | 74.098 | 7115 |
| Senegal | SEN | 2015 | 14578000 | Africa | 66.747 | 2446 |
| Eswatini | SWZ | 2015 | 1104000 | Africa | 55.359 | 7726 |
| Pakistan | PAK | 2015 | 199427008 | Asia | 66.577 | 5056 |
| Gambia | GMB | 2015 | 2086000 | Africa | 60.910 | 1948 |
| Haiti | HTI | 2015 | 10696000 | North America | 62.485 | 1649 |
| Vietnam | VNM | 2015 | 92677000 | Asia | 75.110 | 5733 |
| Congo | COG | 2015 | 4856000 | Africa | 63.097 | 4526 |
| Gabon | GAB | 2015 | 1948000 | Africa | 64.913 | 14315 |
| Nepal | NPL | 2015 | 27015000 | Asia | 69.515 | 2607 |
| North Macedonia | MKD | 2015 | 2079000 | Europe | 75.406 | 13586 |
| Cyprus | CYP | 2015 | 1161000 | Europe | 80.350 | 25903 |
| Country | Code | Year | Population | Continent | Life.Expectency | GDP |
|---|---|---|---|---|---|---|
| Lesotho | LSO | 2015 | 2059000 | Africa | 51.038 | 2954 |
| Armenia | ARM | 2015 | 2926000 | Asia | 74.467 | 9552 |
| Uruguay | URY | 2015 | 3412000 | South America | 77.369 | 19668 |
| Slovakia | SVK | 2015 | 5436000 | Europe | 76.827 | 25896 |
| Bosnia and Herzegovina | BIH | 2015 | 3429000 | Europe | 76.865 | 10305 |
| Mali | MLI | 2015 | 17439000 | Africa | 57.509 | 1563 |
| Romania | ROU | 2015 | 19925000 | Europe | 75.476 | 20549 |
| Denmark | DNK | 2015 | 5689000 | Europe | 80.475 | 44939 |
| Jamaica | JAM | 2015 | 2891000 | North America | 74.098 | 7115 |
| Senegal | SEN | 2015 | 14578000 | Africa | 66.747 | 2446 |
| Eswatini | SWZ | 2015 | 1104000 | Africa | 55.359 | 7726 |
| Pakistan | PAK | 2015 | 199427008 | Asia | 66.577 | 5056 |
| Gambia | GMB | 2015 | 2086000 | Africa | 60.910 | 1948 |
| Haiti | HTI | 2015 | 10696000 | North America | 62.485 | 1649 |
| Vietnam | VNM | 2015 | 92677000 | Asia | 75.110 | 5733 |
| Congo | COG | 2015 | 4856000 | Africa | 63.097 | 4526 |
| Gabon | GAB | 2015 | 1948000 | Africa | 64.913 | 14315 |
| Nepal | NPL | 2015 | 27015000 | Asia | 69.515 | 2607 |
| North Macedonia | MKD | 2015 | 2079000 | Europe | 75.406 | 13586 |
| Cyprus | CYP | 2015 | 1161000 | Europe | 80.350 | 25903 |
| Country | Code | Year | Population | Continent | Life.Expectency | GDP |
|---|---|---|---|---|---|---|
| Lesotho | LSO | 2015 | 2059000 | Africa | 51.038 | 2954 |
| Armenia | ARM | 2015 | 2926000 | Asia | 74.467 | 9552 |
| Uruguay | URY | 2015 | 3412000 | South America | 77.369 | 19668 |
| Slovakia | SVK | 2015 | 5436000 | Europe | 76.827 | 25896 |
| Bosnia and Herzegovina | BIH | 2015 | 3429000 | Europe | 76.865 | 10305 |
| Mali | MLI | 2015 | 17439000 | Africa | 57.509 | 1563 |
| Romania | ROU | 2015 | 19925000 | Europe | 75.476 | 20549 |
| Denmark | DNK | 2015 | 5689000 | Europe | 80.475 | 44939 |
| Jamaica | JAM | 2015 | 2891000 | North America | 74.098 | 7115 |
| Senegal | SEN | 2015 | 14578000 | Africa | 66.747 | 2446 |
| Eswatini | SWZ | 2015 | 1104000 | Africa | 55.359 | 7726 |
| Pakistan | PAK | 2015 | 199427008 | Asia | 66.577 | 5056 |
| Gambia | GMB | 2015 | 2086000 | Africa | 60.910 | 1948 |
| Haiti | HTI | 2015 | 10696000 | North America | 62.485 | 1649 |
| Vietnam | VNM | 2015 | 92677000 | Asia | 75.110 | 5733 |
| Congo | COG | 2015 | 4856000 | Africa | 63.097 | 4526 |
| Gabon | GAB | 2015 | 1948000 | Africa | 64.913 | 14315 |
| Nepal | NPL | 2015 | 27015000 | Asia | 69.515 | 2607 |
| North Macedonia | MKD | 2015 | 2079000 | Europe | 75.406 | 13586 |
| Cyprus | CYP | 2015 | 1161000 | Europe | 80.350 | 25903 |
Colour should be meaningful and take into account the nature of the data being graphed. It should also be attune to colour blindness.
Sequential
Diverging
Qualitative
ColorBrewer https://colorbrewer2.org/